Subspace clustering of high dimensional spatial data with noises

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3056, Issue , 2004, Pages 31-40

  Hsu, Chih Ming ^a   Chen, Ming Syan ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Data clustering; Shannon information; Subspace clustering

Indexed keywords

CLUSTER ANALYSIS; DATA MINING; FILTRATION; COMPUTER PROGRAMMING; DATA ACQUISITION; GRAPHIC METHODS; MATHEMATICAL MODELS; PROBLEM SOLVING;

ATTRIBUTE SELECTION; CURSE OF DIMENSIONALITY; DATA CLUSTERING; DENSITY-BASED CLUSTERING; EQUIVALENT RELATIONSHIP; HIGH DIMENSIONAL DATA; SHANNON INFORMATION; SUB-SPACE CLUSTERING;

CLUSTERING ALGORITHMS; DATA REDUCTION;

DATA CLUSTERING; DATA SETS; SHANNON INFORMATION; SUBSPACE CLUSTERING;

EID: 7444272736     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24775-3_6     Document Type: Conference Paper

References (16)

1. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD’98. 1998.
2. T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley. 1991.
3. B.-R. Dai, C.-R. Lin, and M.-S. Chen. On the techniques for data clustering with numerical constraints. In Proc. Of the 3rd SIAM Intern’l Conference on Data Mining. 2003.
4. A. K. Jain, R C. Dubes. Algorithms for Clustering Data. Prentice Hall. 1988.
5. A. K. Jain, M. N. Murty and P. J. Flynn. Data Clustering: A Review. ACM Computing Surveys. 1999.
6. M. Ester, H. P. Kriegel, J. Sander and X. Xu. A density-based algorithm for discovery in large spatial databases with noise. Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD-96), pages 67-82, Poland, Maine, August 1996.
7. J. B. Fraleigh. A First Course in Abstract Algebra, 6th edition. Addison Wesley. 1999.
8. A. Hinneburg and D. A. Keim. An efficient approach to clustering in large multimedia databases with noise. Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD-98), pages 58-65, New York, August 1998.
9. A. Hinneburg and D. A. Keim. Optimal Grid-Clustering: Towards Breaking the curse of Dimensionality in High-Dimensional Clustering. VLDB, 1999.
10. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann. 2000.
11. J. Han, M. Kamber, and A. Tung. Spatial Clustering Methods in Data Mining: A Survey. In Miller, H., and Han, J., eds., Geographic Data Mining and Knowledge Discovery. Taylor and Francis. 21, 2001.
12. C.-R. Lin and M.-S. Chen. A robust and efficient clustering algorithm based on cohesion self-merging. In Proc. Of the 8th ACM SIGKDD Internal Conf. On Knowledge Discovery and Data Mining, August 2002.
13. E. Schikuta. Grid-Clustering: A Fast Hierarchical Clustering Method for Very Large Data Sets. In Proc. 13th Int. Conf. On Pattern Recognition, Volume 2, pages 101-105, IEEE Computer Society, 1996.
14. G. Sheikholeslami, S. Chatterjee, and A. Zhang. WaveCluster: A multi-resolution clustering approach for very large spatial databases. VLDB, 1998.
15. W. Wang, J. Yang, and R. Muntz. STING: A Statistical Information grid Approach to Spatial Data Mining. VLDB, 1997.
16. O. R. Zaiane, A. Foss, C.-H. Lee, and W. Wang. On data clustering analysis: Scalability, constraints, and validation. In PAKDD 2002, pages 28-39, 2002.